Apressingunmetneedinthefieldofglaucomadiagnosticsistofindmethodsforobjectivedetectionof disease worsening or prediction of visual field (VF) progression in eyes with advanced disease. Eyes with advanced glaucoma are at high risk of losing the remaining vision and blindness. Retinal nerve fiber layer (RNFL) and optic nerve head measures reach their measurement floor as glaucoma progresses beyond the earlystages.Hence,functionalassessmentofthecentralVFiscurrentlythemaintoolformonitoringadvanced glaucoma. Our central hypothesis is that assessment of the macular retinal ganglion cell (RGC)/axonal complexcanleadtoimproveddetectionorpredictionofdiseaseprogressionsincethelastRGCstodisappear in glaucoma reside in the central retina (the macula). We will test this hypothesis in a cohort of glaucoma subjects just reaching 5 years of follow-up and validate our methods in separate cohorts of glaucoma and normal subjects. Aim 1. Are macular thickness measures able to detect change earlier and with a stronger signalcomparedtoRNFLmeasuresinadvancedglaucoma?Wewillmeasureprogressionratesforglobaland local macular and RNFL measures within a Bayesian hierarchical framework. We will compare progression rates and the proportion of progressing eyes/regions/sectors for macular and RNFL measures to normal eyes and account for differing scales, age-related decay, and treatment. Aim 2A. Can macular OCT thickness changes confirm and predict changes in central VFs for advanced glaucoma? We will estimate longitudinal/temporal structure-function relationships with Bayesian joint hierarchical longitudinal modeling of macularOCTandcentral10VFmeasures.Thesemodelswilldeterminewhetherthereisacontemporaneous or lagged deterioration of OCT and VF. We will assess the influence of baseline disease severity, treatment and other covariates on these joint longitudinal models. We will also compare the joint macular/central VF models to joint models of RNFL and 24 VFs and develop functional prediction models from 1 to 4 years ahead. Aim 2B. To validate the performance of prediction models, we will initiate a second prospectively enrolledcohortofpatientsmeetingsimilarinclusioncriteriaandmatchedtotheoriginalcohortbyage,gender, ethnicity and baseline glaucoma severity. We will compare VF point predictions (e.g., one- or two-visit step ahead)totheobservedVFdata.Aim 3.Developsoftwareforcombiningmacularstructuralandfunctionaldata in real time as a clinical tool for detection or prediction of progression. It will provide clinicians with structural/functional rates of change and structural ?step? changes from baseline, and the probability and distributionof predicted functional changes The informationprovided by theapplication can be used duringa clinical encounter to make decisions regarding ongoing management of glaucoma. Widespread real-time use ofoursoftwarewillresultinsignificantimprovementsindiseasemonitoringandtimelytreatmentofprogressive glaucomathroughadvancedstagesandwillhelpreducevisualdisabilityfromglaucoma.